Portail d'apprentissage - L'appariement sur score de propension

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class="node node--type-toolkit node--view-mode-full"> <div class="node__content"> <div class="container"> <div class="row align-items-center"> <div class="col-12 col-lg-8"> <span class="overline"> Portail d'apprentissage </span> <h1><span class="field field--name-title field--type-string field--label-hidden">Portail d'apprentissage - L'appariement sur score de propension</span> </h1> <ul class="list-inline" data-br="" id="responsive-list"> <li class="list-inline-item"> <a role="button" href="/taxonomy/term/2_fr" class="btn btn--strand btn-evaluation-hover"> <span class="focus-evaluation" aria-hidden="true"></span> <span class="btn__text ms-2"> 脡valuation </span> <span class="cap-icon bi-chevron-right btn__icon" aria-hidden="true"></span> </a> </li> <li class="list-inline-item"> <a role="button" href="/topics/evaluation_fr" class="btn btn--strand"> <span class="me-2 tag"> <span class="tag-icon tag-icon--outlined" aria-hidden="true"> <span class="cap-icon bi-tag" aria-hidden="true"></span> </span> <span>脡valuation</span> </span> <span class="cap-icon bi-chevron-right btn__icon" aria-hidden="true"></span> </a> </li> <li class="list-inline-item"> <a role="button" href="/topics/evaluation-methods_fr" class="btn btn--strand"> <span class="me-2 tag"> <span class="tag-icon tag-icon--outlined" aria-hidden="true"> <span class="cap-icon bi-tag" aria-hidden="true"></span> </span> <span>M茅thodes d'茅valuation</span> </span> <span class="cap-icon bi-chevron-right btn__icon" aria-hidden="true"></span> </a> </li> <li class="list-inline-item"> <a role="button" href="/topics/data-management_fr" class="btn btn--strand"> <span class="me-2 tag"> <span class="tag-icon tag-icon--outlined" aria-hidden="true"> <span class="cap-icon bi-tag" aria-hidden="true"></span> </span> <span>Gestion des donn茅es</span> </span> <span class="cap-icon bi-chevron-right btn__icon" aria-hidden="true"></span> </a> </li> <li class="list-inline-item"> <a role="button" href="/topics/performance-monitoring-and-evaluation-framework-pmef_fr" class="btn btn--strand"> <span class="me-2 tag"> <span class="tag-icon tag-icon--outlined" aria-hidden="true"> <span class="cap-icon bi-tag" aria-hidden="true"></span> </span> <span>Cadre de Suivi et d'脡valuation de la Performance (CSEP)</span> </span> <span class="cap-icon bi-chevron-right btn__icon" aria-hidden="true"></span> </a> </li> <li class="list-inline-item"> <a role="button" href="/topics/monitoring-and-evaluation-framework_fr" class="btn btn--strand"> <span class="me-2 tag"> <span class="tag-icon tag-icon--outlined" aria-hidden="true"> <span class="cap-icon bi-tag" aria-hidden="true"></span> </span> <span>Cadre de suivi et d'茅valuation.聽</span> </span> <span class="cap-icon bi-chevron-right btn__icon" aria-hidden="true"></span> </a> </li> <li class="list-inline-item toggle-more-items"> <button class="btn bg-transparent p-0"> <span class="badge rounded-pill bg-primary--light"> <span class="cap-icon bi-three-dots icon--black" aria-hidden="true"></span> </span> <span class="visually-hidden">Show/hide other elements.</span> </button> </li> </ul> <p class="lead">L'appariement par score de propension (PSM) est une m茅thode d'茅valuation de pointe dont la principale caract茅ristique est de minimiser le biais de s茅lection dans les 茅valuations de programmes, caus茅 par des caract茅ristiques observables. Il permet d'茅tablir des comparaisons pr茅cises et fiables entre les participants et les non-participants 脿 un programme, tout en renfor莽ant la cr茅dibilit茅 des r茅sultats.</p> </div> </div> </div> <div class="container my-5"> <div class="row"> <div class="bg-xs-sm-light col-12 col-md-4 col-xl-3 d-print-none mb-5 mb-md-0 py-3 py-md-0"> <h3 class="mb-3 mb-md-4 fs-5 d-md-none">Page contents</h3> <nav data-scrollspy="in_page_navigation" class="navbar navbar-light navbar--scrollspy flex-column align-items-stretch p-0" id="in_page_navigation"> <nav class="nav nav-pills flex-column"> <a class="nav-link" href="#paragraph-110290">Principes de base</a> <nav class="nav nav-pills flex-column"> <a class="nav-link" href="#paragraph-110291">En bref</a> <a class="nav-link" href="#paragraph-110292">Avantages et inconv茅nients</a> <a class="nav-link" href="#paragraph-110293">Quand l'utiliser ?</a> <a class="nav-link" href="#paragraph-110294">脡tape par 茅tape</a> <a class="nav-link" href="#paragraph-110295">Principaux points 脿 retenir</a> </nav> <a class="nav-link" href="#paragraph-110296">Tirer les le莽ons de la pratique</a> <a class="nav-link" href="#paragraph-110298">Pour en savoir plus</a> </nav> </nav> </div> <div class="col-12 col-md-8 col-xl-9" data-bs-spy="scroll" data-bs-target="#in_page_navigation" data-bs-root-margin="0px 0px -85%"> <section class="pb-5 pb-md-6-5" id="paragraph-110288"> <div class="container"> <div class="row row-cols-1 row-cols-md-1 justify-content-start align-items-stretch gy-3 gy-md-5"> <div id="paragraph-110289" class="col paragraph paragraph--type--image paragraph--view-mode--default"> <div class="field field--name-media field--type-entity-reference field--label-hidden field__items"> <div class="field__item"><figure class="media media--type-image media--view-mode-wide"> <div class="rounded-3-img mb-3 field field--name-field-media-image field--type-image field--label-hidden field__item"> <picture> <source srcset="/sites/default/files/styles/full_width_desktop_1920/public/2024-06/three-farmers-with-laptop.jpg.webp?itok=hSzUYSLR 1x" media="(min-width: 992px)" type="image/webp" width="1020" height="551"/> <source srcset="/sites/default/files/styles/full_width_mobile_1080/public/2024-06/three-farmers-with-laptop.jpg.webp?itok=dp9pa1ye 1x" media="(min-width: 576px)" type="image/webp" width="1020" height="551"/> <img loading="lazy" src="/sites/default/files/styles/full_width_mobile_1080/public/2024-06/three-farmers-with-laptop.jpg.webp?itok=dp9pa1ye" width="1020" height="551" alt="Three farmers with laptop" class="img-fluid" /> </picture> </div> </figure> </div> </div> </div> </div> </div> </section> <section class="py-5 py-md-6-5" id="paragraph-110290"> <div class="container"> <h2 id="paragraph-110290-title" class="mb-3 mb-md-4">Principes de base</h2> <div class="row row-cols-1 row-cols-md-1 justify-content-start align-items-stretch gy-3 gy-md-5"> <div id="paragraph-110291" class="col paragraph paragraph--type--textarea paragraph--view-mode--default"> <h3 id="paragraph-110291-title" class="mb-3 mb-md-4">En bref</h3> <div class="field field--name-text-formatted field--type-text-long field--label-hidden"> <p>Les m茅thodes d'appariement font partie de la famille des m茅thodes d'茅valuation quasi-exp茅rimentale. La technique de l'appariement par score de propension (PSM) est actuellement l'un des outils les plus avanc茅s et les plus efficaces appliqu茅s 脿 l'茅valuation de divers programmes. On peut distinguer deux types d'approches de l'appariement des scores de propension :</p> <ol> <li>le PSM standard, conventionnel ou binaire ; et</li> <li><a href="/training/evaluation-learning-portal/learning-portal-generalised-propensity-score-matching-gpsm_fr" data-entity-type="node" data-entity-uuid="0948cc0f-74bd-40e5-a291-7c47615727c7" data-entity-substitution="canonical" title="Portail d'apprentissage - Appariement des scores de propension g茅n茅ralis茅s (GPSM)">le PSM g茅n茅ralis茅</a>.</li> </ol> <h4><strong>Minimiser les biais</strong></h4> <p>Le PSM est une approche quasi-exp茅rimentale puissante qui permet de minimiser le biais de s茅lection d茅coulant des caract茅ristiques observables des participants et des non-participants. Le biais de s茅lection se produit lorsque les participants 脿 un programme sont syst茅matiquement diff茅rents des non-participants. Ces diff茅rences syst茅miques peuvent 锚tre dues 脿 des caract茅ristiques sp茅cifiques des participants et des non-participants, qui peuvent 锚tre observ茅es 脿 l'aide de variables disponibles dans les sources de donn茅es correspondantes (observables). Par exemple, les exploitations agricoles de certains secteurs ou ayant une envergure 茅conomique ou physique sp茅cifique peuvent 锚tre plus motiv茅es pour participer 脿 un programme. Ce biais peut 锚tre minimis茅 en appariant les participants avec les non-participants qui, sur la base de leurs caract茅ristiques observables, avaient la m锚me probabilit茅 de participer 脿 un programme mais ont d茅cid茅 de ne pas le faire. Cette probabilit茅 est mesur茅e par ce que l'on appelle le score de propension, qui est calcul茅 pour chaque unit茅 sur la base d'un ensemble de caract茅ristiques observables non affect茅es par un programme.</p> <h4><strong>Comparer les comparables</strong></h4> <p>Dans le cas d'un PSM binaire, l'id茅e est de trouver des unit茅s qui sont similaires, d'un point de vue observationnel, entre un groupe de non-participants et un groupe de participants au programme, en termes de caract茅ristiques ant茅rieures au programme. Chaque participant est appari茅脿 un ou plusieurs non-participants similaires d'un point de vue observationnel, sur la base de leurs scores de propension correspondants. La diff茅rence moyenne de r茅sultats entre les deux groupes est ensuite calcul茅e pour estimer l'effet de traitement moyen du programme. Dans la pratique, diff茅rentes techniques ou algorithmes peuvent 锚tre utilis茅s pour apparier les participants et les non-participants sur la base du score de propension. Il s'agit notamment de l'appariement avec le plus proche voisin (NN), de l'appariement par compas et rayon (<em>radius matching</em>), de l'appariement par stratification et par intervalle, de l'appariement par noyau ou de l'appariement lin茅aire local (LLM).</p> </div> </div> <div id="paragraph-110292" class="col paragraph paragraph--type--textarea paragraph--view-mode--default"> <h3 id="paragraph-110292-title" class="mb-3 mb-md-4">Avantages et inconv茅nients</h3> <div class="field field--name-text-formatted field--type-text-long field--label-hidden"> <table class="table table-striped table-bordered table-responsive table--capnet"> <thead> <tr> <th> <p class="text-align-justify">Avantages</p> </th> <th> <p class="text-align-justify">Inconv茅nients</p> </th> </tr> </thead> <tbody> <tr> <td> <div class="arrow-list"> <ul> <li>Efficace pour trouver des groupes de contr么le appropri茅s (contrefactuels).</li> <li>Fonctionne bien lorsque la participation au programme est affect茅e uniquement par les caract茅ristiques observables d'une unit茅 (par exemple, l'exploitation agricole, la personne, la communaut茅, la r茅gion).</li> <li>Si le biais de s茅lection d没 aux caract茅ristiques non observ茅es est probablement n茅gligeable, le mod猫le PSM offre une bonne comparaison avec les estimations randomis茅es.</li> <li>Permet un calcul simple d'indices tels que ATT (effet moyen du traitement sur les personnes trait茅es), ATE (effet moyen du traitement) et ATNT (effet moyen du traitement sur les personnes non trait茅es).</li> </ul> </div></td> <td> <ul> <li>Repose fortement sur l'hypoth猫se que les caract茅ristiques observables utilis茅es pour estimer le score de propension expliquent toutes les diff茅rences entre les unit茅s soutenues et le groupe de comparaison avant la mise en 艙uvre du programme.</li> <li>Ne peut pas produire de r茅sultats plausibles en cas d'omission de caract茅ristiques observables expliquant les diff茅rences de performance.</li> <li>Le PSM est une m茅thode qui n茅cessite beaucoup de donn茅es.</li> </ul> </td> </tr> </tbody> </table> </div> </div> <div id="paragraph-110293" class="col paragraph paragraph--type--textarea paragraph--view-mode--default"> <h3 id="paragraph-110293-title" class="mb-3 mb-md-4">Quand l'utiliser ?</h3> <div class="field field--name-text-formatted field--type-text-long field--label-hidden"> <p>Cette technique peut 锚tre utilis茅e lorsque l'on dispose de suffisamment de donn茅es avant la mise en 艙uvre d'un programme pour apparier les participants et les non-participants. Elle suppose que la probabilit茅 de participer 脿 un programme est principalement influenc茅e par des caract茅ristiques qui peuvent 锚tre observ茅es et d茅crites 脿 l'aide de variables correspondantes disponibles pour les deux groupes. En outre, cette technique ne peut 锚tre utilis茅e que lorsqu'il est possible de calculer la valeur d'un ou de plusieurs indicateurs d'impact au moment de l'茅valuation et non avant la mise en 艙uvre d'un programme.</p> <p>En appliquant une m茅thode de PSM binaire pour identifier un groupe de contr么le valide, il faut s'assurer qu'il n'y a pas de diff茅rences syst茅miques dans les caract茅ristiques non observ茅es entre les unit茅s soutenues par un programme et les unit茅s de comparaison appari茅es qui pourraient influencer le r茅sultat. Cette m茅thode ne produira pas de r茅sultats raisonnables si d'autres caract茅ristiques observables importantes ne sont pas incluses dans le mod猫le qui explique les diff茅rences.</p> <h4>Conditions pr茅alables</h4> <div class="arrow-list"> <ul> <li>Bonne compr茅hension des conditions d茅terminant la probabilit茅 de participer 脿 un programme.</li> <li>Donn茅es abondantes sur les participants et les non-participants au programme avant sa mise en 艙uvre, ce qui permet d'observer les principales caract茅ristiques qui affectent la probabilit茅 de participation 脿 un programme.</li> <li>Capacit茅脿 calculer la valeur des indicateurs d'impact pour les groupes appari茅s de participants et de non-participants au cours d'une 茅valuation.</li> <li>Comp茅tences quantitatives 茅lev茅es de l'茅valuateur.</li> </ul> <p>La technique peut 锚tre appliqu茅e pour 茅valuer l'effet du soutien de la PAC sur l'茅volution des valeurs des indicateurs d'impact 茅num茅r茅s dans le tableau suivant.</p> <table class="table table-striped table-bordered table-responsive table--capnet"> <thead> <tr> <th><strong>Indicateur d'impact du PDR</strong></th> <th><strong>Indicateur d'impact du plan strat茅gique relevant de la PAC</strong></th> </tr> </thead> <tbody> <tr> <td>I.01 - Revenu d'entreprise agricole</td> <td>I.2 - Evolution du revenu agricole par rapport 脿 l'茅conomie g茅n茅rale</td> </tr> <tr> <td>I.02 - Revenu des facteurs agricoles</td> <td>I.3 - 脡volution du revenu agricole</td> </tr> <tr> <td> </td> <td>I.4 - Evolution du niveau de revenu agricole par type d'exploitation (par rapport 脿 la moyenne agricole)</td> </tr> <tr> <td> </td> <td>I.5 - Evolution du revenu agricole dans les zones 脿 contraintes naturelles (par rapport 脿 la moyenne)</td> </tr> <tr> <td>I.03 - Productivit茅 totale des facteurs dans l'agriculture</td> <td>I.6 - Productivit茅 totale des facteurs dans l'agriculture</td> </tr> <tr> <td>I.07 - 脡missions de l'agriculture</td> <td> <p>I.10 - 脡missions de gaz 脿 effet de serre provenant de l'agriculture</p> <p>I.14 - 脡missions d'ammoniac provenant de l'agriculture</p> </td> </tr> <tr> <td>I.08 - Indice des oiseaux des terres agricoles</td> <td>I.19 - Indice des oiseaux des terres agricoles</td> </tr> <tr> <td>I.09 - Agriculture 脿 haute valeur naturelle (HNV)</td> <td> </td> </tr> <tr> <td>I.10 - Pr茅l猫vements d'eau dans l'agriculture</td> <td>I.17 - Indice d'exploitation de l'eau plus (WEI+)</td> </tr> <tr> <td>I.11 - Qualit茅 de l'eau</td> <td> <p>I.15 - Bilan brut des 茅l茅ments nutritifs sur les terres agricoles</p> <p>I.16 - Nitrates dans les eaux souterraines</p> </td> </tr> <tr> <td>I.13 - Erosion des sols par l'eau</td> <td>I.13 - Pourcentage de terres agricoles soumises 脿 une 茅rosion mod茅r茅e et s茅v猫re des sols</td> </tr> <tr> <td>I.14 - Taux d'emploi rural</td> <td>I.24 - 脡volution du taux d'emploi dans les zones rurales, incluant une d茅composition selon les sexes</td> </tr> <tr> <td>I.15 - Degr茅 de pauvret茅 rurale</td> <td>I.27 - 脡volution de l'indice de pauvret茅 en zone rurale</td> </tr> <tr> <td>I.16 - PIB rural par habitant</td> <td>I.25 - 脡volution du produit int茅rieur brut (PIB) par habitant dans les zones rurales</td> </tr> </tbody> </table> <p>En ce qui concerne l'indice des oiseaux des terres agricoles, une approche d'茅valuation 脿 l'茅chelle du champ/de la parcelle (niveau micro) peut 锚tre r茅alis茅e 脿 l'aide du programme de surveillance des oiseaux communs, si les donn茅es disponibles sont suffisantes. Pour ce faire, il convient d'appliquer la technique du PSM afin de mettre en relation les b茅n茅ficiaires et les non-b茅n茅ficiaires, puis de comparer l'effet moyen du soutien de la PAC sur la biodiversit茅 dans chaque groupe. Au niveau macro茅conomique, il est 茅galement recommand茅 d'appliquer la technique du PSM pour d茅terminer l'effet des aides de la PAC sur la biodiversit茅 au niveau des quadrants utilis茅s pour observer les populations d'oiseaux des terres agricoles dans le cadre du programme de surveillance des oiseaux communs. Les quadrants peuvent 锚tre utilis茅s comme unit茅s fonctionnelles pour l'indice des oiseaux des terres agricoles et calcul茅s ult茅rieurement par zones biog茅ographiques (diff茅rents habitats agricoles) ou au niveau r茅gional sur la base de donn茅es g茅or茅f茅renc茅es.</p> <p>En ce qui concerne les indicateurs mesurant l'emploi, le PIB par habitant et le taux de pauvret茅 dans les zones rurales, les groupes de b茅n茅ficiaires et de contr么le peuvent 锚tre construits sur la base des r茅gions g茅ographiques soutenues et non soutenues, id茅alement LAU 2 (v茅rifier si dans NUTS2) telles que d茅finies par la typologie urbaine-rurale d'Eurostat. Dans ce cas, l'acc猫s 脿 une source compl猫te de donn茅es statistiques et d'informations sur les caract茅ristiques des r茅gions g茅ographiques avant la mise en 艙uvre du programme est n茅cessaire.</p> </div> </div> </div> <div id="paragraph-110294" class="col paragraph paragraph--type--textarea paragraph--view-mode--default"> <h3 id="paragraph-110294-title" class="mb-3 mb-md-4">脡tape par 茅tape</h3> <div class="field field--name-text-formatted field--type-text-long field--label-hidden"> <div class="arrow-list"> <ul> <li><strong>脡tape 1</strong> - Trouvez un 茅chantillon de b茅n茅ficiaires (par exemple, exploitations agricoles, agriculteurs, entreprises non agricoles, communaut茅s, zones, r茅gions, etc.) dans une base de donn茅es disponible (par exemple, le RICA) et utilisez le syst猫me 茅lectronique de suivi et d'茅valuation (par exemple, le syst猫me d'information 茅lectronique de l'<a href="https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32013R1305#d1247e4833-487-1">article 70, r猫glement (UE) 1305/2013</a>, ou celui de l'<a href="https://eur-lex.europa.eu/legal-content/EN/TXT/?toc=OJ:L:2021:435:TOC&uri=uriserv:OJ.L_.2021.435.01.0001.01.ENG#d1e9493-1-1">article 130, r猫glement (UE) 2021/2115</a>) comme point de r茅f茅rence.</li> <li><strong>脡tape 2</strong> - Dans la m锚me base de donn茅es (par exemple le RICA), s茅lectionner toutes les unit茅s pertinentes qui n'ont pas re莽u d'aide ou qui ont re莽u un niveau d'aide arbitrairement bas au cours de la m锚me p茅riode (non-b茅n茅ficiaires). Dans ce dernier cas, il peut 锚tre pr茅f茅rable de d茅finir le faible niveau de soutien en utilisant non pas les niveaux absolus de soutien, mais plut么t les niveaux de soutien pond茅r茅s par une autre variable, comme la superficie agricole utilis茅e par l'agriculteur ou la superficie ou la population d'une r茅gion g茅ographique.</li> <li><strong>脡tape 3</strong> - Dans un groupe de non-b茅n茅ficiaires, identifier les unit茅s qui ne pouvaient pas remplir les conditions d'茅ligibilit茅 du programme (en raison d'un revenu 茅lev茅, de la taille, de la localisation, etc) et les retirer de l'analyse.</li> <li><strong>脡tape 4</strong> - Collecter des donn茅es pour toutes les unit茅s des deux groupes (b茅n茅ficiaires et non-b茅n茅ficiaires) sur leurs principales caract茅ristiques (variables) au d茅but d'une p茅riode de mise en 艙uvre. Il convient de noter que les variables incluses dans l'analyse doivent affecter 脿 la fois la s茅lection d'une unit茅 et les indicateurs calcul茅s au niveau micro茅conomique (par exemple, les indicateurs d'impact). Certaines des variables propos茅es (utilis茅es comme variables de contr么le importantes) peuvent 锚tre : <ul> <li>le niveau de soutien re莽u par une unit茅 donn茅e au cours des p茅riodes de programmation pr茅c茅dentes ; et/ou</li> <li>le niveau de soutien re莽u par une unit茅 donn茅e de la part d'autres sources publiques (par exemple, les fonds structurels de l'UE, ou issus du pilier I) au cours de la p茅riode analys茅e.</li> </ul> </li> <li><strong>脡tape 5</strong> - Appliquer les techniques appropri茅es pour identifier un groupe de contr么le ad茅quat 脿 partir de l'茅chantillon de non-b茅n茅ficiaires (voir 茅tapes 2-3), dont les membres ont la m锚me propension 脿 participer 脿 un programme (certains des non-b茅n茅ficiaires et/ou des b茅n茅ficiaires seront exclus de l'analyse en raison de l'absence d'unit茅s de contr么le ad茅quates).</li> <li><strong>脡tape 6</strong> - V茅rifier statistiquement la similitude des deux groupes avant qu'ils ne b茅n茅ficient d'un soutien de la part d'un programme (par exemple, en effectuant des tests statistiques sur les covariables incluses dans l'analyse). La valeur moyenne d'une unit茅 dans le groupe b茅n茅ficiaire ne doit pas diff茅rer de mani猫re significative de l'unit茅 correspondante dans le groupe de contr么le. Une fois que le groupe de b茅n茅ficiaires et le groupe de contr么le ont 茅t茅 constitu茅s, l'effet net de l'aide peut 锚tre estim茅 au cours des 茅tapes suivantes.</li> <li><strong>脡tape 7</strong> - Calculer la valeur moyenne de l'indicateur (des indicateurs) d'impact commun(s) ou suppl茅mentaire(s) pour le groupe de b茅n茅ficiaires et le groupe de contr么le au moment de l'茅valuation.</li> <li><strong>脡tape 8</strong> - Calculer l'effet net en tant que diff茅rence dans la valeur moyenne de l'indicateur d'impact commun ou additionnel entre le groupe de b茅n茅ficiaires et le groupe de contr么le (moyenne traitement sur trait茅).</li> <li><strong>脡tape 9</strong> - Effectuer une analyse de sensibilit茅 (par exemple, l'approche des <em>Rosenbaum bounds</em>) pour 茅valuer les effets possibles des 茅l茅ments non observables sur les r茅sultats obtenus.</li> <li><strong>脡tape 10</strong> - Agr茅ger les r茅sultats et calculer les effets du soutien de la PAC sur les indicateurs d'impact analys茅s au niveau macro茅conomique et au niveau de la zone de programmation. Dans cette 茅tape, l'茅valuateur doit calculer les effets directs nets du soutien de la PAC sur les indicateurs d'impact au niveau de la zone de programmation en appliquant des techniques d'extrapolation (c'est-脿-dire en multipliant les micro-r茅sultats moyens calcul茅s 脿 un niveau micro par le nombre de b茅n茅ficiaires/non-b茅n茅ficiaires).</li> </ul> </div> </div> </div> <div id="paragraph-110295" class="col paragraph paragraph--type--textarea paragraph--view-mode--default"> <h3 id="paragraph-110295-title" class="mb-3 mb-md-4">Principaux points 脿 retenir</h3> <div class="field field--name-text-formatted field--type-text-long field--label-hidden"> <div class="arrow-list"> <ul> <li>Le PSM se distingue par sa capacit茅脿 r茅duire les biais de s茅lection en comparant les participants et les non-participants sur la base de caract茅ristiques observables.</li> <li>Le PSM binaire se concentre sur la recherche de non-participants similaires aux participants en ce qui concerne les caract茅ristiques ant茅rieures au programme, tandis que le PSM g茅n茅ralis茅茅tend cette comparaison.</li> <li>Le PSM contr么le efficacement le biais de s茅lection en appariant les groupes sur la base de leurs scores de propension, calcul茅s 脿 partir des caract茅ristiques observables.</li> <li>Le PSM est tr猫s utile pour 茅valuer l'efficacit茅 de divers programmes, en particulier dans les contextes o霉 les donn茅es ant茅rieures au programme sont abondantes.</li> <li>Les indices ATT, ATE et ATNT sont calcul茅s pour mesurer l'impact du programme sur les participants et sur l'ensemble de la population.</li> </ul> </div> </div> </div> </div> </div> </section> <section class="py-5 py-md-6-5" id="paragraph-110296"> <div class="container"> <h2 id="paragraph-110296-title" class="mb-3 mb-md-4">Tirer les le莽ons de la pratique</h2> <div class="row row-cols-1 row-cols-md-1 justify-content-start align-items-stretch gy-3 gy-md-5"> <div id="paragraph-110297" class="col paragraph paragraph--type--content-collection paragraph--view-mode--default"> <div> <div class="card card--download-modal-alt"> <div class="card-body d-flex"> <span class="cap-icon bi-link text-primary me-2" aria-hidden="true"></span> <div class="pb-3"> <span class="overline d-inline-block mb-2"> Publication - Factsheets | <time datetime="2020-03-01T12:00:00Z" class="datetime">01 Mar 2020</time> </span> <a class="card-title--link" href="/publications/assessing-rdp-effects-water-abstraction-mixed-methods-approach-greek-region-thessaly_fr"> <h3 class="h6 card-title"> Assessing RDP Effects on Water Abstraction: A Mixed Methods Approach from the Greek Region of Thessaly </h3> </a> </div> </div> </div> </div> </div> </div> </div> </section> <section class="py-5 py-md-6-5" id="paragraph-110298"> <div class="container"> <h2 id="paragraph-110298-title" class="mb-3 mb-md-4">Pour en savoir plus</h2> <div class="row row-cols-1 row-cols-md-1 justify-content-start align-items-stretch gy-3 gy-md-5"> <div id="paragraph-110299" class="col paragraph paragraph--type--textarea paragraph--view-mode--default"> <div class="field field--name-text-formatted field--type-text-long field--label-hidden"> <div class="arrow-list"> <ul> <li><a href="https://enrd.ec.europa.eu/enrd-static/app_templates/enrd_assets/pdf/evaluation/epe_master.pdf">R茅seau europ茅en de la PAC pour le d茅veloppement rural (2014)</a><br>Saisir la r茅ussite de votre PDR : Lignes directrices pour l'茅valuation ex post des PDR 2007-2013</li> </ul> </div> </div> </div> </div> </div> </section> </div> </div> </div> </div> </article> </div> </div> <div class="region region-bottom-content"> <section id="block-social-share" class="container my-5 my-md-7"> <script type="application/json">{"service":"sbkm","version":"2.0","stats":true,"color":true,"more":["e-mail","facebook","linkedin","twitter","whatsapp"]}</script> <script type="application/json">{"service":"etrans","languages":{"exclude":["fr"]},"renderAs":"link","domain":"gen","delay":0,"config":{"live":true}}</script> 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